In the art there are known computer systems in which generating, storing and/or processing of data does not take place at the same location. Therefore, an amount of data to be transmitted forms a bottle-neck despite increasing resources and can lead to a significant restriction of efficiency of the whole system.
To reduce the amount of data to be transmitted there are known various data compression methods. It can generally be distinguished between methods using lossy source coding and methods using loss-free source coding. Source coding is conversion of a data word in a corresponding data word using of a redundancy decreasing code. In loss-free source coding transmitted data can be fully restored by a suitable decompression, whereas this is not the case in lossy source coding.
Currently used adaptive data compression methods are for example JPEG and MPEG, wherein these are applied to all data of a data set. These adaptive data compression methods fit on a mostly fluctuating statistic of the data. Therefore, these methods are often called “context sensitive”. Either the transmitter or the receiver determines a medium or minimal quality of reconstruction for all data or a data rate before transmission, respectively. All data are then transmitted in correspondence with the determined quality or data rate, respectively.
Furthermore, there are known so-called progressive data compression methods that usually are based on so-called wavelets. These progressive data compression methods allow for a piece-by-piece reconstruction of data, wherein the receiver can determine which transmitted amount of data is sufficient. During transmission it can be determined when transmission of data is canceled. Therefore, an amount of data to be transmitted can be decreased. In general, all of the data is transmitted until transmission of data is canceled.
Independent of data compression, data mining methods (i.e., methods that relate to a directed or subject-related search of data) allow for data sets of interest to be searched for in a database. Here, the term “of interest” is limited to identification of data sets having similar features. Detection of data sets of interest normally takes place by monitored training using examples.
However, it is common to all aforementioned methods that, on the one hand, only data are transmitted and, on the other hand, compression of data is limited to such a degree that the receiver can meaningfully process and/or use transmitted data. The respective used transmission channel is consequently not used effectively, thereby leading to a bottle-neck on the transmission channel.